Mean and covariance properties of dynamic PET reconstructions from list-mode data

We derive computationally efficient methods for the estimation of the mean and variance properties of penalized likelihood dynamic positron emission tomography (PET) images. This allows us to predict the accuracy of reconstructed activity estimates and to compare reconstruction algorithms theoretica...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2006-01, Vol.25 (1), p.42-54
Main Authors: Asma, E., Leahy, R.M.
Format: Article
Language:English
Subjects:
Algorithms
Analysis of variance
Artificial Intelligence
Data Interpretation, Statistical
Databases, Factual
Dynamic PET
Fisher information matrix
Image analysis
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image reconstruction
Image resolution
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Likelihood Functions
Positron emission tomography
Positron-Emission Tomography - methods
Reproducibility of Results
Sensitivity and Specificity
Signal processing
Smoothing methods
Spatial resolution
Spatiotemporal phenomena
uniform resolution
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Summary:We derive computationally efficient methods for the estimation of the mean and variance properties of penalized likelihood dynamic positron emission tomography (PET) images. This allows us to predict the accuracy of reconstructed activity estimates and to compare reconstruction algorithms theoretically. We combine a bin-mode approach in which data is modeled as a collection of independent Poisson random variables at each spatiotemporal bin with the space-time separabilities in the imaging equation and penalties to derive rapidly computable analytic mean and variance approximations. We use these approximations to compare bias/variance properties of our dynamic PET image reconstruction algorithm with those of multiframe static PET reconstructions.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2005.859716